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PubMed2001

Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.

Mass segmentation is used as the first step in many computer-aided diagnosis (CAD) systems for classification of breast masses as malignant or benign. The goal of this paper was to study the accuracy of an automated mass segmentation method developed in our laboratory, and to investigate the effect of the segmentation stage on the overall classification accuracy. The automated segmentation method was quantitatively compared with manual segmentation by two expert radiologists (R1 and R2) using three similarity or distance measures on a data set of 100 masses. The area overlap measures between R1 and R2, the computer and R1, and the computer and R2 were 0.76 +/- 0.13, 0.74 +/- 0.11, and 0.74 +/- 0.13, respectively. The interobserver difference in these measures between the two radiologists was compared with the corresponding differences between the computer and the radiologists. Using three similarity measures and data from two radiologists, a total of six statistical tests were performed. The difference between the computer and the radiologist segmentation was significantly larger than the interobserver variability in only one test. Two sets of texture, morphological, and spiculation features, one based on the computer segmentation, and the other based on radiologist segmentation, were extracted from a data set of 249 films from 102 patients. A classifier based on stepwise feature selection and linear discriminant analysis was trained and tested using the two feature sets. The leave-one-case-out method was used for data sampling. For case-based classification, the area Az under the receiver operating characteristic (ROC) curve was 0.89 and 0.88 for the feature sets based on the radiologist segmentation and computer segmentation, respectively. The difference between the two ROC curves was not statistically significant.

Algorithms↗
PubMed2004

An automated tissue preclassification approach for telepathology: implementation and performance analysis.

Telepathology is generally defined as the use of telecommunications technologies in the practice of anatomic or surgical pathology. In the usual telepathology scenario, a remotely located pathologist views images of tissues samples in order to render a diagnosis of the biopsy. Some telepathology systems involve interactive remote control of a microscope-based imaging system which delivers diagnostic quality imagery to the remote pathologist. The usefulness of such interactive systems depends on minimizing the end-to-end delays involved in controlling the robotic microscope, manipulating the tissue sample, and acquiring and transmitting the high-resolution image. An approach to minimizing end-to-end delay involves adding "intelligence" to the image acquisition system so that it can gather, classify, rank, and transmit diagnostically useful images in a semiautonomous fashion. In this research, we develop image analysis and ranking techniques which can improve the end-to-end performance of a robotic telepathology imaging system. Our semiautonomous image collection system uses morphological techniques to extract seed points for suspicious regions, a novel region growing algorithm to segment the regions of interest, and heuristically motivated expert system ranking techniques to select diagnostically relevant "next-step" image acquisitions. Diagnostic relevance of our segmentation and ranking algorithms is established via subjective and objective testing of the system. In subjective testing, pathologists Agree or Strongly Agree that all segmented regions are diagnostically relevant with probability greater than 0.75. In objective testing, 84% of "next-step" images acquired by our algorithms coincide with the areas most likely to be chosen by a pathologist.

Artificial Intelligence↗
PubMed1981

Within-day physiologic variation of leukocyte types in healthy subjects as assayed by two automated leukocyte differential analyzers.

The physiologic within-day (700-2200 h) variation of leukocyte-type concentrations in blood as determined for 21 healthy young adults is reported. All blood specimens were obtained in duplicate such that the within-batch analytic variation, as well as the pertinent biologic sources of variation, was able to be determined. All specimens were analyzed on each of two automated leukocyte differential systems: Hemalog-D Differential System and the Hematrak-240 Analyzer. On the basis of a comparison of the performances of the two analyzers, it was decided to report the neutrophil and the lymphocyte values as measured on both systems, the monocyte values as measured on teh Hematrak only, and the eosinophil and basophil values as measured on the Hemalog-D only. The intrasubject within-day physiologic variations for the cell on the Hemalog-D and Hematrak, respectively, in terms of coefficient of variation wee as follows: neutrophils 19.4% and 19.6%; lymphocytes, 13.8% and 17.5%. For monocytes, as measured on the Hematrak, it was 13.4%. For eosinophils and basophils, as measured on the Hemalog-D, it was 27.2% and 8.3%, respectively. There was a consistent group-specific diurnal variance that amounted to more than 40% of the total within-day variance, both for lymphocytes and for eosinophils. The within-day physiologic variation of the cell type concentrations for eight of the volunteers was compared with that of the plasma cortisol concentrations, as determined on specimens derived from the same veni-puncture sessions. The decrease in plasma cortisol values was in most instances associated with decreases in eosinophils and increases in neutrophils. For total leukocytes and neutrophils, the mean concentration for smokers was significantly higher than that for nonsmokers.

Adult↗
PubMed2002

Heuristics for identification of acronym-definition patterns within text: towards an automated construction of comprehensive acronym-definition dictionaries.

OBJECTIVES: To develop an automated, accurate and scalable method by which acronym-definition pairs can be identified within text. Its primary advantage is in enabling information processing methods to resolve author-defined acronyms, but it also allows an automated creation of a reference work on acronym definitions. This has several advantages over manual or semi-automated methods, besides time and effort saved, such as enabling identification of relative frequencies for alternate acronyms and definitions as well as spelling, phrasing and hyphenation variants for a unique acronym-definition pair. It also aids users in identifying acronym/definition variants present in the literature that may not necessarily be in biomedical databases. METHODS: A set of heuristics to accurately locate and identify the boundaries of acronym-definition pairs was developed and refined in terms of precision and recall on subsets of MEDLINE records. These training sets were gradually increased in size and heuristics re-evaluated to ensure scalability. RESULTS: Our final set of Acronym Resolving General Heuristics (ARGH) had a sample-based estimated rate of 96.5 +/- 0.4% precision and 93.0 +/- 2.7% recall when tested on over 12 million MEDLINE records, identifying more than 174,000 unique acronyms and their 737,000 associated definitions. CONCLUSIONS: We estimate that as much as 36% of the acronyms in MEDLINE are associated with more than one definition and, conversely, up to 10% of definitions are associated with more than one acronym. The number of unique acronyms in MEDLINE is increasing at a rate of approximately 11,000 per year, while the number of definitions associated with them is growing at approximately four times that rate. Access to the ARGH database is available online at http://lethargy.swmed.edu/ARGH/argh.asp. The heuristic module and database are available upon request.

Abbreviations as Topic↗
PubMed2005

An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy.

The behavior of white and red blood cells, platelets, and circulating injected particles is one of the most studied areas of physiology. Most methods used to analyze the circulatory patterns of cells are time consuming. We describe a system named CellTrack, designed for fully automated tracking of circulating cells and micro-particles and retrieval of their behavioral characteristics. The task of automated blood cell tracking in vessels from in vivo video is particularly challenging because of the blood cells' nonrigid shapes, the instability inherent in in vivo videos, the abundance of moving objects and their frequent superposition. To tackle this, the CellTrack system operates on two levels: first, a global processing module extracts vessel borders and center lines based on color and temporal patterns. This enables the computation of the approximate direction of the blood flow in each vessel. Second, a local processing module extracts the locations and velocities of circulating cells. This is performed by artificial neural network classifiers that are designed to detect specific types of blood cells and micro-particles. The motion correspondence problem is then resolved by a novel algorithm that incorporates both the local and the global information. The system has been tested on a series of in vivo color video recordings of rat mesentery. Our results show that the synergy between the global and local information enables CellTrack to overcome many of the difficulties inherent in tracking methods that rely solely on local information. A comparison was made between manual measurements and the automatically extracted measurements of leukocytes and fluorescent microspheres circulatory velocities. This comparison revealed an accuracy of 97%. CellTrack also enabled a much larger volume of sampling in a fraction of time compared to the manual measurements. All these results suggest that our method can in fact constitute a reliable replacement for manual extraction of blood flow characteristics from in vivo videos.

Algorithms↗
PubMed2006

Constrained Gaussian mixture model framework for automatic segmentation of MR brain images.

An automated algorithm for tissue segmentation of noisy, low-contrast magnetic resonance (MR) images of the brain is presented. A mixture model composed of a large number of Gaussians is used to represent the brain image. Each tissue is represented by a large number of Gaussian components to capture the complex tissue spatial layout. The intensity of a tissue is considered a global feature and is incorporated into the model through tying of all the related Gaussian parameters. The expectation-maximization (EM) algorithm is utilized to learn the parameter-tied, constrained Gaussian mixture model. An elaborate initialization scheme is suggested to link the set of Gaussians per tissue type, such that each Gaussian in the set has similar intensity characteristics with minimal overlapping spatial supports. Segmentation of the brain image is achieved by the affiliation of each voxel to the component of the model that maximized the a posteriori probability. The presented algorithm is used to segment three-dimensional, T1-weighted, simulated and real MR images of the brain into three different tissues, under varying noise conditions. Results are compared with state-of-the-art algorithms in the literature. The algorithm does not use an atlas for initialization or parameter learning. Registration processes are therefore not required and the applicability of the framework can be extended to diseased brains and neonatal brains.

Algorithms↗
PubMed1997

A comparison of the accuracy and reproducibility of digital three-dimensional coronary artery reconstructions using edge detection or videodensitometry.

Global quantitative three-dimensional measurements of coronary arteries may be helpful in determining the functional significance of various forms of coronary pathology. A computerized system has been developed that is capable of performing 3-D reconstruction of digitized images obtained from multiple coronary angiographic views using either automated edge detection (AED) or videodensitometric (VD) techniques. To compare the accuracy and reproducibility of measurements obtained from this system using either technique, stationary and moving coronary aluminum 3-D phantoms, each with 13 branches (diameter 0.58-6.35 mm, length 21.5-64.5 mm), were imaged and reconstructed 10 separate times each. Individual branch lengths and diameters were calculated and compared to each other and to known values. Diameter measurements were compared using either AED or VD. Intraclass correlation coefficients between observed values (ICC) for vessel length were r = 0.89 for the stationary and r = 0.97 for the moving phantom. ICCs for vessel diameter were r = 0.93 (AED) and r = 0.95 (VD) for the stationary and r = 0.98 (AED) and r = 0.97 (VD) for the moving phantom. Mean differences (+/-SD) between true and observed values [MDTO(+/-SD)] for vessel length were -1.0 +/- 3.9 mm for the stationary and -3.5 +/- 3.2 mm for the moving phantom. MDTO(+/-SD) for vessel diameter were -0.10 +/- 0.52 mm (AED) and +0.03 +/- 0.30 mm (VD) for the stationary and -0.21 +/- 0. 44 mm (AED) and -0.12 +/- 0.33 (VD) for the moving phantom. We conclude that the quantitative accuracy and reproducibility of measurements obtained by computerized 3-D reconstruction of coronary model phantoms is of high enough quality to warrant further clinical evaluation. VD appears to be more accurate than AED for measuring vessel diameter.

Aluminum↗
PubMed2005

Region-based supine-prone correspondence for the reduction of false-positive CAD polyp candidates in CT colonography.

RATIONALE AND OBJECTIVES: Radiologists often compare the supine and prone data sets of a patient to confirm potential polyp findings in computed tomographic (CT) colonography (CTC). We developed a new automated method that uses region-based supine-prone correspondence for the reduction of false-positive (FP) polyp candidates in computer-aided detection (CAD) for CTC. MATERIALS AND METHODS: Up to six anatomic landmarks are established by use of the extracted region of the colonic lumen. A region-growing scheme with distance calculations is used to divide the colonic lumen into overlapping segments that match in the supine and prone data sets. Polyp candidates detected by means of a CAD scheme are eliminated in colonic segments that have sufficient diagnostic quality and contain polyp candidates in only one of the data sets of a patient. The method was evaluated with 121 CTC cases, including 42 polyps of 5 mm or greater in 28 patients, obtained by use of single- and multidetector CT scanners with standard pre-colonoscopy cleansing. RESULTS: Complete or partial correspondence was established in 71% of cases. Based on a leave-one-patient-out evaluation, application of the method reduced 19% of FP results reported by our CAD scheme at a 90.5% by-polyp detection sensitivity, without loss of any true-positive results. The resulting CAD scheme yielded 2.4 FP results per patient, on average, with the use of the correspondence method, whereas it yielded 3.0 FP results per patient without the use of the method. CONCLUSION: The correspondence method is potentially useful for improving the specificity of CAD in CTC.

Colonic Polyps↗
PubMed2004

A quantitative analysis of 3-D coronary modeling from two or more projection images.

A method is introduced to examine the geometrical accuracy of the three-dimensional (3-D) representation of coronary arteries from multiple (two and more) calibrated two-dimensional (2-D) angiographic projections. When involving more then two projections, (multiprojection modeling) a novel procedure is presented that consists of fully automated centerline and width determination in all available projections based on the information provided by the semi-automated centerline detection in two initial calibrated projections. The accuracy of the 3-D coronary modeling approach is determined by a quantitative examination of the 3-D centerline point position and the 3-D cross sectional area of the reconstructed objects. The measurements are based on the analysis of calibrated phantom and calibrated coronary 2-D projection data. From this analysis a confidence region (alpha degrees approximately equal to [35 degrees - 145 degrees]) for the angular distance of two initial projection images is determined for which the modeling procedure is sufficiently accurate for the applied system. Within this angular border range the centerline position error is less then 0.8 mm, in terms of the Euclidean distance to a predefined ground truth. When involving more projections using our new procedure, experiments show that when the initial pair of projection images has an angular distance in the range alpha degrees approximately equal to [35 degrees - 145 degrees], the centerlines in all other projections (gamma = 0 degrees - 180 degrees) were indicated very precisely without any additional centering procedure. When involving additional projection images in the modeling procedure a more realistic shape of the structure can be provided. In case of the concave segment, however, the involvement of multiple projections does not necessarily provide a more realistic shape of the reconstructed structure.

Algorithms↗
PubMed2001

Pseudo-outer product based fuzzy neural network fingerprint verification system.

Fingerprint identification has been used in law enforcement applications over the last century, and has become the de facto international standard for positive identification. With the emergence of automated fingerprint identification technologies, it has assisted in making the once labour-intensive process of classifying, searching and matching a thing of the past. As a biometrics proof of identification, not many have ventured into the world of fingerprint identification using fuzzy neural networks. In this paper, a database of fingerprint images is constructed and a fuzzy neural network called the pseudo outer product fuzzy neural network (POPFNN) [Zhou, R.W. & Quek, C. (1996). A pseudo outer-product based fuzzy neural network. Neural Networks, 9(9), 1569-1581] is trained to detect similarity between two fingerprints and decide whether they belong to the same person. The fundamental idea is that, given a person's fingerprints taken under different conditions, the POPFNN based fingerprint verification system should be sufficiently robust to distinguish the difference. The people providing the fingerprint samples are subjected to different 'adverse' conditions; from wetness to chemical treatments. Fingerprint images are taken after conditions such as: after a shower, holding pineapples (mild acid from fruit), after washing one's hands, etc. The characteristics of POPFNN, such as the learning, generalisation, and high computational abilities, make fingerprint verification particularly powerful when verifying authentic fingerprints subjected to external conditions and recognising spurious ones. In order to demonstrate the efficacy of POPFNN and its application in the fingerprint verification system (FVS), several types of experiments have been designed and implemented in this work. The experimental results and analysis are presented at the end of the paper for discussion.

Algorithms↗
PubMed2005

A three-dimensional registration method for automated fusion of micro PET-CT-SPECT whole-body images.

Micro positron emission tomography (PET) and micro single-photon emission computed tomography (SPECT), used for imaging small animals, have become essential tools in developing new pharmaceuticals and can be used, among other things, to test new therapeutic approaches in animal models of human disease, as well as to image gene expression. These imaging techniques can be used noninvasively in both detection and quantification. However, functional images provide little information on the structure of tissues and organs, which makes the localization of lesions difficult. Image fusion techniques can be exploited to map the functional images to structural images, such as X-ray computed tomography (CT), to support target identification and to facilitate the interpretation of PET or SPECT studies. Furthermore, the mapping of two functional images of SPECT and PET on a structural CT image can be beneficial for those in vivo studies that require two biological processes to be monitored simultaneously. This paper proposes an automated method for registering PET, CT, and SPECT images for small animals. A calibration phantom and a holder were used to determine the relationship among three-dimensional fields of view of various modalities. The holder was arranged in fixed positions on the couches of the scanners, and the spatial transformation matrix between the modalities was held unchanged. As long as objects were scanned together with the holder, the predetermined matrix could register the acquired tomograms from different modalities, independently of the imaged objects. In this work, the PET scan was performed by Concorde's microPET R4 scanner, and the SPECT and CT data were obtained using the Gamma Medica's X-SPECT/CT system. Fusion studies on phantoms and animals have been successfully performed using this method. For microPET-CT fusion, the maximum registration errors were 0.21 mm +/- 0.14 mm, 0.26 mm +/- 0.14 mm, and 0.45 mm +/- 0.34 mm in the X (right-left), Y (upper lower), and Z (rostral-caudal) directions, respectively; for the microPET-SPECT fusion, they were 0.24 mm +/- 0.14 mm, 0.28 mm +/- 0.15 mm, and 0.54 mm +/- 0.35 mm in the X, Y, and Z directions, respectively. The results indicate that this simple method can be used in routine fusion studies.

Algorithms↗
PubMed2005

A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications.

In this paper, we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs). The classifier is part of a computer-aided diagnosis (CADx) scheme that is aimed to assisting radiologists in making more accurate diagnoses of breast cancer on mammograms. The methods we considered were: support vector machine (SVM), kernel Fisher discriminant (KFD), relevance vector machine (RVM), and committee machines (ensemble averaging and AdaBoost), of which most have been developed recently in statistical learning theory. We formulated differentiation of malignant from benign MCs as a supervised learning problem, and applied these learning methods to develop the classification algorithm. As input, these methods used image features automatically extracted from clustered MCs. We tested these methods using a database of 697 clinical mammograms from 386 cases, which included a wide spectrum of difficult-to-classify cases. We analyzed the distribution of the cases in this database using the multidimensional scaling technique, which reveals that in the feature space the malignant cases are not trivially separable from the benign ones. We used receiver operating characteristic (ROC) analysis to evaluate and to compare classification performance by the different methods. In addition, we also investigated how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD, and RVM) yielded the best performance (Az = 0.85, SVM), significantly outperforming a well-established, clinically-proven CADx approach that is based on neural network (Az = 0.80).

Algorithms↗
PubMed2005

Towards automated cellular image segmentation for RNAi genome-wide screening.

The Rho family of small GTPases is essential for morphological changes during normal cell development and migration, as well as during disease states such as cancer. Our goal is to identify novel effectors of Rho proteins using a cell-based assay for Rho activity to perform genome-wide functional screens using double stranded RNA (dsRNAs) interference. We aim to discover genes could cause the cell phenotype changed dramatically. Biologists currently attempt to perform the genome-wide RNAi screening to identify various image phenotypes. RNAi genome-wide screening, however, could easily generate more than a million of images per study, manual analysis is thus prohibitive. Image analysis becomes a bottleneck in realizing high content imaging screens. We propose a two-step segmentation approach to solve this problem. First, we determine the center of a cell using the information in the DNA-channel by segmenting the DNA nuclei and the dissimilarity function is employed to attenuate the over-segmentation problem, then we estimate a rough boundary for each cell using a polygon. Second, we apply fuzzy c-means based multi-threshold segmentation and sharpening technology; for isolation of touching spots, marker-controlled watershed is employed to remove touching cells. Furthermore, Voronoi diagrams are employed to correct the segmentation errors caused by overlapping cells. Image features are extracted for each cell. K-nearest neighbor classifier (KNN) is employed to perform cell phenotype classification. Experimental results indicate that the proposed approach can be used to identify cell phenotypes of RNAi genome-wide screens.

Algorithms↗
PubMed2005

A 2-D/3-D model-based method to quantify the complexity of microvasculature imaged by in vivo multiphoton microscopy.

This paper presents model-based information-theoretic methods to quantify the complexity of tumor microvasculature, taking into account shape, textural, and structural irregularities. The proposed techniques are completely automated, and are applicable to optical slices (3-D) or projection images (2-D). Improvements upon the prior literature include: (i) measuring local (vessel segment) as well as global (entire image) vascular complexity without requiring explicit segmentation or tracing; (ii) focusing on the vessel boundaries in the complexity estimate; and (iii) added robustness to image artifacts common to tumor microvasculature images. Vessels are modeled using a family of super-Gaussian functions that are based on the superquadric modeling primitive common in computer vision. The superquadric generalizes a simple ellipsoid by including shape parameters that allow it to approximate a cylinder with elliptical cross-sections (generalized cylinder). The super-Gaussian is obtained by composing a superquadric with an exponential function giving a form that is similar to a standard Gaussian function but with the ability to produce level sets that approximate generalized cylinders. Importantly, the super-Gaussian is continuous and differentiable so it can be fit to image data using robust non-linear regression. This fitting enables quantification of the intrinsic complexity of vessel data vis-a-vis the super-Gaussian model within a minimum message length (MML) framework. The resulting measures are expressed in units of information (bits). Synthetic and real-data examples are provided to illustrate the proposed measures.

Algorithms↗
PubMed2006

Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy.

A fully automated approach is presented for robust detection and classification of changes in longitudinal time-series of color retinal fundus images of diabetic retinopathy. The method is robust to: 1) spatial variations in illumination resulting from instrument limitations and changes both within, and between patient visits; 2) imaging artifacts such as dust particles; 3) outliers in the training data; 4) segmentation and alignment errors. Robustness to illumination variation is achieved by a novel iterative algorithm to estimate the reflectance of the retina exploiting automatically extracted segmentations of the retinal vasculature, optic disk, fovea, and pathologies. Robustness to dust artifacts is achieved by exploiting their spectral characteristics, enabling application to film-based, as well as digital imaging systems. False changes from alignment errors are minimized by subpixel accuracy registration using a 12-parameter transformation that accounts for unknown retinal curvature and camera parameters. Bayesian detection and classification algorithms are used to generate a color-coded output that is readily inspected. A multiobserver validation on 43 image pairs from 22 eyes involving nonproliferative and proliferative diabetic retinopathies, showed a 97% change detection rate, a 3% miss rate, and a 10% false alarm rate. The performance in correctly classifying the changes was 99.3%. A self-consistency metric, and an error factor were developed to measure performance over more than two periods. The average self consistency was 94% and the error factor was 0.06%. Although this study focuses on diabetic changes, the proposed techniques have broader applicability in ophthalmology.

Algorithms↗
PubMed2006

Shape-constraint region growing for delineation of hepatic metastases on contrast-enhanced computed tomograph scans.

OBJECTIVES: The objectives of this study were to develop a shape-constraint region-growing algorithm to automatically delineate liver metastases on computed tomography images and to compare automated tumor measurements with those outlined manually by radiologists. METHODS: The algorithm starts with a manual selection of a seed lesion region of interest (ROI). Based on intensity distributions of the seed ROI and the liver parenchyma, several parameters are computed and used to adaptively guide the region growing. To prevent the region growing from leaking into surrounding tissues of similar characteristics, specific shape constraints, including a local shape, a global shape, and a gravity-shift index, are developed to jointly control the iteration of the region growing. The algorithm was applied to 59 lesions in 14 patients with liver metastases. The maximal diameter (unidimension), the product of the maximal and maximal perpendicular diameters (bidimension), and the area in the axial plane were calculated for each of the segmented lesions. Three independent radiologists manually measured all the lesions once, and one of the radiologists measured each lesion 3 times. For each measurement, the concordance correlation coefficient (CCC) was used to assess the pairwise agreement between the computer and the different radiologists, and the overall concordance correlation coefficient (OCCC) was used to assess the agreement between the computer and the multiple radiologists and between the one radiologist's 3 readings. RESULTS: Fifty-three of 59 (89.8%) lesions in 14 patients with liver metastases were successfully segmented using this algorithm. The algorithm achieved a median accuracy of 88.0%. CCCs/OCCCs ranged from 0.943 to 0.999 with 95% confidence intervals. CONCLUSIONS: High accuracy and CCCs/OCCCs suggested that measurements made by the computer were very similar to those made by the radiologists.

Algorithms↗
PubMed2005

On the automated definition of mobile target volumes from 4D-CT images for stereotactic body radiotherapy.

Stereotactic body radiotherapy (SBRT) can be used to treat small lesions in the chest. A vacuum-based immobilization system is used in our clinic for SBRT, and a motion envelope is used in treatment planning. The purpose of this study is to automatically derive motion envelopes using deformable image registration of 4D-CT images, and to assess the effect of abdominal pressure on the motion envelopes. 4D-CT scans at ten phases were acquired prior to treatment for both free and restricted breathing using a vacuum-based immobilization system that includes an abdominal pressure pillow. To study the stability of the motion envelope over the course of treatment, a mid-treatment 4D-CT scan was obtained after delivery of the third fraction for two patients. The planning target volume excluding breathing motion (PTV(ex)) was defined on the image set at full exhalation phase and transformed into all other phases using displacement maps from deformable image registration. The motion envelope was obtained as the union of PTV(ex) masks of all phases. The ratios of the motion envelope to PTV(ex) volume ranged from 1.3 to 2.5. When pressure was applied, the ratios were reduced by as much as 29% compared to free breathing for some patients, but increased by up to 9% for others. The abdominal pressure pillow has more motion restriction effects on the anterior/inferior region of the lung. For one of the two patients for whom the 4D-CT scan was repeated at mid-treatment, the motion envelope was reproducible. However, for the other patient the tumor location and lung motion pattern significantly changed due to changes in the anatomy surrounding the tumor during the course of treatment, indicating that an image-guided approach to SBRT may increase the efficacy of this treatment.

Algorithms↗
PubMed2006

ESTimating plant phylogeny: lessons from partitioning.

BACKGROUND: While Expressed Sequence Tags (ESTs) have proven a viable and efficient way to sample genomes, particularly those for which whole-genome sequencing is impractical, phylogenetic analysis using ESTs remains difficult. Sequencing errors and orthology determination are the major problems when using ESTs as a source of characters for systematics. Here we develop methods to incorporate EST sequence information in a simultaneous analysis framework to address controversial phylogenetic questions regarding the relationships among the major groups of seed plants. We use an automated, phylogenetically derived approach to orthology determination called OrthologID generate a phylogeny based on 43 process partitions, many of which are derived from ESTs, and examine several measures of support to assess the utility of EST data for phylogenies. RESULTS: A maximum parsimony (MP) analysis resulted in a single tree with relatively high support at all nodes in the tree despite rampant conflict among trees generated from the separate analysis of individual partitions. In a comparison of broader-scale groupings based on cellular compartment (ie: chloroplast, mitochondrial or nuclear) or function, only the nuclear partition tree (based largely on EST data) was found to be topologically identical to the tree based on the simultaneous analysis of all data. Despite topological conflict among the broader-scale groupings examined, only the tree based on morphological data showed statistically significant differences. CONCLUSION: Based on the amount of character support contributed by EST data which make up a majority of the nuclear data set, and the lack of conflict of the nuclear data set with the simultaneous analysis tree, we conclude that the inclusion of EST data does provide a viable and efficient approach to address phylogenetic questions within a parsimony framework on a genomic scale, if problems of orthology determination and potential sequencing errors can be overcome. In addition, approaches that examine conflict and support in a simultaneous analysis framework allow for a more precise understanding of the evolutionary history of individual process partitions and may be a novel way to understand functional aspects of different kinds of cellular classes of gene products.

Databases, Genetic↗